Head and neck squamous cell carcinoma (HNSCC) is the most common head and neck cancer and is highly aggressive and heterogeneous, leading to variable prognosis and immunotherapy outcomes. Circadian rhythm alterations in tumourigenesis are of equal importance to genetic factors and several biologic clock genes are considered to be prognostic biomarkers for various cancers. The aim of this study was to establish reliable markers based on biologic clock genes, thus providing a new perspective for assessing immunotherapy response and prognosis in patients with HNSCC.We used 502 HNSCC samples and 44 normal samples from the TCGA-HNSCC dataset as the training set. 97 samples from GSE41613 were used as an external validation set. Prognostic characteristics of circadian rhythm-related genes (CRRGs) were established by Lasso, random forest and stepwise multifactorial Cox. Multivariate analysis revealed that CRRGs characteristics were independent predictors of HNSCC, with patients in the high-risk group having a worse prognosis than those in the low-risk group. The relevance of CRRGs to the immune microenvironment and immunotherapy was assessed by an integrated algorithm.6-CRRGs were considered to be strongly associated with HNSCC prognosis and a good predictor of HNSCC. The riskscore established by the 6-CRRG was found to be an independent prognostic factor for HNSCC in multifactorial analysis, with patients in the low-risk group having a higher overall survival (OS) than the high-risk group. Nomogram prediction maps constructed from clinical characteristics and riskscore had good prognostic power. Patients in the low-risk group had higher levels of immune infiltration and immune checkpoint expression and were more likely to benefit from immunotherapy.6-CRRGs play a key predictive role for the prognosis of HNSCC patients and can guide physicians in selecting potential responders to prioritise immunotherapy, which could facilitate further research in precision immuno-oncology. GloballThe circadian clock system is used by organisms to adjust their biochemical and behavioral processes to the cyclical environmental changes caused by the rotation of the Earth , 13. TheWith the continuous development of bioinformatics, a large quantity of articles was constructing prognostic models of diseases by filtering the prognostic markers of the features through methods as machine learning –31. In o22.1https://portal.gdc.cancer.gov/). The level 3 HTSeq-Fragments per kilobase million (FPKM) data of TCGA-HNSCC was converted to TPM (transcripts per million reads) according to the following formula: TPMn = FPKMn * 106/(FPKM0 +… + FPKMm), where n represented gene n and m represented the total number of all genes, respectively. Then, we performed a log2-based transformation of TPM. After excluding samples lacking complete clinical information, 501 HNSCC samples were included in the subsequent analysis. The sample size of HNSCC patients at the M stage varied greatly. This stage was consequently excluded from the analysis. We obtained 97 HNSCC patients with complete follow-up information from GSE41613 as an external validation set. In this study, two separate datasets, TCGA-HNSCC and GSE41613, were merged and the batch effect was corrected for using the ComBat function in the R package “sva” in order to eliminate the batch effect in the microarray expression data. We obtained Circadian rhythm-associated 1424 genes by Genecards (https://www.genecards.org/)  was compared between the two groups using Kaplan-Meier curves created by survminer in the R package. The “pec” R package was used for PCA analysis and c-index calculation. The prediction accuracy of the signature was evaluated by constructing a time-dependent ROC using the “timeROC” R package.2.4Univariate Cox regression and multivariate Cox regression analysis were used to evaluate whether risk score was an independent prognostic factor. The rms R package combined with risk scores and clinicopathological characteristics was used to construct a nomogram for predicting the probability of survival at 1, 3, and 5 years in HNSCC patients.2.5The “c2.cp.kegg.v7.4.symbols” obtained from the MSigDB database were analyzed using the “GSVA” R package. Gene Ontology (GO) analysis is done using the Cluster-Profiler R package.2.6http://biocc.hrbmu.edu.cn/TIP/) (http://research-pub.gene.com/IMvigor210CoreBiologies/).Currently recognized methods, including XCELL , 34, TIMcn/TIP/)  and the cn/TIP/) . Using tcn/TIP/)  to calcu2.7https://portal.gdc.cancer.gov/). We analyze mutation data from HNSCC samples using maftools (^6 (http://bioinfo.life.hust.edu.cn/web/GSCALite/) provides an online cancer genomic analysis platform by integrating 33 cancer data from TCGA and normal tissue genomics data from GTEx . Detailed cell type annotations are provided at the single cell level for further analysis of specific gene expression in different cell types. The specific gene expression in different cell types further reveals the variation of TME in patients with different HNSCC, thus explaining to some extent the heterogeneity of HNSCC.A single-cell RNA sequencing database focused on the tumor microenvironment (TME) is housed at the Tumor Immunization Single Cell Center (TISCH)(2.9https://www.cancerrxgene.org/) was used to measure half-maximal inhibitory concentrations (IC50). This is achieved by using the pRRophetic R package.GDSC (Genomics of Drug Sensitivity in Cancer)  < 0.05 was considered statistically significant.Statistical analyses were performed using R software v4.1.3. Kaplan-Meier (KM) survival curves and log-rank tests were used to compare overall survival (OS) in the high-risk and low-risk groups. The LASSO regression analysis and RF were used to screen candidate CRRGs. Stepwise multifactor Cox regression analysis was used to construct CRRGs characteristics. Time-dependent ROC was used to assess the predictive performance of the model. Spearman correlation analysis was used to assess the correlation between risk score and immune cell infiltration. Wilcox test was used to compare the proportion of TIICs, immune checkpoints, and immune function between the two groups. 33.1The primary design of this study can be known from the graphical flow chart Figure 13.2Two machine learning algorithms were applied to screen signature genes among biological clock genes associated with the prognosis of HNSCC patients. For the LASSO algorithm, cross-validation was performed. The minimum criteria for constructing the LASSO classifier were selected to identify 28 feature genes , Age (≤65 and >65 years), gender , pathological grading (G1-2 and G3-4), N-stage (N0-1 and N2-3), T-stage (T1-2 and T3-4) and HPV status (positive and negative). Notably, in all subgroups, low-risk patients had a significant survival advantage of longer survival time compared to high-risk patients Figure 53.5P<0.01), T stage (P<0.01), stage (P<0.01), and N stage (P<0.05), but not with age, or gender  were all prognostic factors significantly associated with patient prognosis in the univariate cox analysis (P<0.001) and N stage (P=0.002) remained significant  analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on DEGs to elucidate the information pathways and underlying molecular mechanisms of the risk signature. After filtering the data according to thresholds , we could obtain enrichment results that were significantly different in the HNSCC and normal groups. Cellular fractions (CC) mainly include immunoglobulin complex, external side of plasma membrane and immunoglobulin complex, circulating, etc. Molecular functions (MF) include mainly antigen binding and immunoglobulin receptor binding. Biological processes (BP) include immunoglobulin production, regulation of B cell activation, B cell receptor signaling pathway and humoral immune response, etc.  than in the low-risk group (88.98%) (3.9P=7.2e-06)  are one of the important components of TME, their composition, and distribution are inextricably linked to the process of tumor development. First, we explored the correlation between risk score and infiltrating immune cell abundance based on a total of seven different algorithms, EPIC, XCELL, CIBERSORT, MCPCOUNTER, QUANTISEQ, CIBERSORT-ABS and TIMER, where CD4+ T cell infiltration was positively correlated with risk score in most algorithms , Docetaxel (P=0.00062), Gefitinib (P=3.7e-07), 5-Fluorouracil (P=5.7e-07), Lapatinib (P=1.7e-05) and Paclitaxel (P=1.1e-10) possessed higher IC50 values. In contrast, among the remaining six chemical or targeted agents IGF1R_3801 (P=2.9e-09), Erlotinib (P=0.00062), Camptothecin (P=2.1e-07), Cisplatin (P=0.00034), Palbociclib (P=8.8e-05) Gemcitabine (P=2.2e-08) had higher IC50 values at higher risk.On the basis of risk scores, to evaluate the potential biological value of 6-CRRGs as biomarkers for predicting response to clinical drug therapy in patients with HNSCC, we analyzed the IC50 values of 198 drugs between high and low risk groups of patients with TCGA-HNSCC by the “pRRophetic” R package. We found 142 chemotherapeutic or targeted agents with significantly different chemosensitivity. In 4Despite the heavy health burden of HNSCC on society, circadian rhythm-associated genes have lacked systematic studies in HNSCC. Therefore, we constructed a multigene prognostic model based on circadian rhythm-related genes based on the TCGA-HNSCC dataset. In this study, we selected 6-CRRGs to construct a new prognostic model using Lasso, RF, and univariate and multifactor COX risk regression analyses. The signature of the 6-CRRGs we constructed proved to be an independent prognostic factor for HNSCC. We found significant prognostic differences between the two groups. ROC curve and calibration curve analyses demonstrated the outstanding predictive performance of the 6-CRRGs signature. In addition, we constructed a nomogram and found that the 6-CRRGs signature has better predictive efficacy compared to clinicopathological features, which can help clinicians to make more accurate prognostic judgments for HNSCC patients.via the MMP1/PI3K/Akt axis in patients with oral squamous cell carcinoma (It is now well established that adenosine is able to bind to A2AR and thereby limit the antitumor activity of CD4+ T cells, CD8+ T cells, and NK cells, and that it induces the expression of CTLA-4 and PD-1 to promote immune escape , 56. ADAarcinoma .TME has become a consensus as a key factor influencing the development of cancer, and the immune microenvironment should be noted as one of the main features of TME. Treg cells, as key cells regulating immune responses and maintaining self-tolerance, cause CD8+ T cell dysfunction in TME in HNSCC –68. In aHigh tumor mutational load is usually considered to be strongly associated with a good prognosis due to increased immunogenicity of tumor-specific antigenic targets and tumor-infiltrating immune cells . HoweverAlthough the 6-CRRGs signature we constructed has promising value in predicting the prognosis of HNSCC patients and helping clinicians with treatment selection, we still need to acknowledge that this study has some limitations. Firstly, our study was based on the analysis of data from public databases, which may lead to deviations in predictions from the actual situation, although we have taken several approaches to try to avoid this. There is still a need to collect a large amount of clinical information on HNSCC patients in the clinic as well as sequencing data after receiving immunotherapy in order to validate the accuracy of the practical application of the model and the accuracy of predicting response to immunotherapy and chemotherapy. Finally, we did not consider tumor location in this study because HNSCC contains too many sites of incidence, and the number of patients varies too much from site to site. In TCGA, some of the tumors had only single-digit sample sizes and could not be studied. This is, of course, one of the limitations of our study.5In summary, we systematically explored 6-CRRGs signature for HNSCC and successfully constructed a prognostic signature of 6-CRRGs, which can accurately assess the prognosis and immune status of HNSCC patients and help clinicians identify specific sub-groups of patients who may benefit from immunotherapy and chemotherapy for personalized treatment.The original contributions presented in the study are included in the article/GT and ZX conceived the study. HC, JY, and GP drafted the manuscript. HC, JZ, and XX performed the literature search and collected the data. GS, XX, and JZ analyzed and visualized the data. JL, ZX, and GT helped with the final revision of this manuscript. All authors contributed to the article and approved the submitted version.


Meningioma is the most common primary intracranial tumor. DNA methylation-based subtyping, while highly useful for diagnosis and treatment planning, is costly and not widely available. Therefore, the identification of methylation classes based on histological sections would be highly beneficial as it could greatly support and accelerate diagnostic and treatment decisions. We developed and systematically evaluated an AI framework to perform the classification of the most prevalent methylation subtypes based on histological sections. The model achieved a balanced accuracy of 0.870 for benign-1 vs benign-2 and 0.749 for benign-1 vs. intermediate-A in a narrow validation set. Combined with the network’s assessed focus on key tumor regions these results provide a promising proof-of-concept of such an AI-driven classification approach in precision medicine.Convolutional neural networks (CNNs) are becoming increasingly valuable tools for advanced computational histopathology, promoting precision medicine through exceptional visual decoding abilities. Meningiomas, the most prevalent primary intracranial tumors, necessitate accurate grading and classification for informed clinical decision-making. Recently, DNA methylation-based molecular classification of meningiomas has proven to be more effective in predicting tumor recurrence than traditional histopathological methods. However, DNA methylation profiling is expensive, labor-intensive, and not widely accessible. Consequently, a digital histology-based prediction of DNA methylation classes would be advantageous, complementing molecular classification. In this study, we developed and rigorously assessed an attention-based multiple-instance deep neural network for predicting meningioma methylation classes using tumor methylome data from 142 (+51) patients and corresponding hematoxylin-eosin-stained histological sections. Pairwise analysis of sample cohorts from three meningioma methylation classes demonstrated high accuracy in two combinations. The performance of our approach was validated using an independent set of 51 meningioma patient samples. Importantly, attention map visualization revealed that the algorithm primarily focuses on tumor regions deemed significant by neuropathologists, offering insights into the decision-making process of the CNN. Our findings highlight the capacity of CNNs to effectively harness phenotypic information from histological sections through computerized images for precision medicine. Notably, this study is the first demonstration of predicting clinically relevant DNA methylome information using computer vision applied to standard histopathology. The introduced AI framework holds great potential in supporting, augmenting, and expediting meningioma classification in the future. Medical images and their accompanying metadata contain a wealth of shared and complementary information. By leveraging computer vision techniques, the shared subspace between these modalities can be harnessed, while complementary information can contribute to the improvement of precision medicine through the integration of different information modalities. In parallel, computational pathology has gained prominence in the diagnosis of oncological diseases in recent years, primarily due to advancements in artificial intelligence (AI) ,2,3,4,5.In pioneering studies, deep-learning approaches have been applied to aid histological diagnosis or predict molecular information from standard HE-stained tissue slides. For instance, a weakly supervised deep-learning algorithm developed by Campanella et al.  was ableHere, we present the results of a proof-of-principle investigation, using whole-slide-images (WSI) and an attention-based multiple-instance learning approach, to predict methylation classes in meningiomas by employing retrospective in-house datasets. We applied AI-assisted morphological analysis of standard hematoxylin-eosin-stained (HE) tissue sections to distinguish the most prevalent methylation classes of meningiomas  . NotablyTumor samples of patients with confirmed histological diagnosis of meningioma according to the WHO 2021 grading scheme  were useStandard hematoxylin-eosin staining was performed on 3–4 µm thin formalin-fixed, paraffin-embedded (FFPE) tissue sections using the Ventana benchmark Ultra  automated stainer. The glass slides were digitized using a Hamamatsu Nanozoomer S360  at 40× equivalent magnification resulting in WSIs with a resolution of 0.23 µm/px. As predictions are intractable at the WSI level due to memory restrictions, a bag of smaller patches was generated from the WSIs, which are called tiles. Each tile has a size of 256 × 256 pixels, without overlap between the tiles. This size was chosen according to the encoder network and the available memory. Tiles were extracted at native 40× equivalent magnification and from downsampled 20× equivalent magnification of the WSI for each patient.Only patients with available WSI and methylation classification data were considered, and with a methylation classification probability score above 0.5, according to the Meningioma Classifier ,32, wereh QuPath  on the WImportantly, all three classes belong to methylation Group A, along with the rarely diagnosed methylation class benign-3. Group B consists of the methylation classes intermediate-B and the malignant class. Thus, an analysis of specimens from the methylation classes benign-1, benign-2, and intermediate-A provides an ideal setup to initially test the analytical performance of AI-assisted morphological diagnosis for the following reasons: (i) the number of samples per methylation class allows for an assessment of patient diversity, (ii) the cohort sizes are similar, and (iii) the relative similarity of methylomes  coupled with differences in predominant histological and genetic features allows for a critical assessment of resolution performance. The number of patient samples in each dataset is shown in As a preprocessing step, the background was filtered out via Otsu’s thresholding  and morpTo predict the methylation class of a patient, we utilized a two-class weakly supervised learning setting, with the WSI as input data. As it is infeasible to feed in the high-resolution image at once, we made use of a multiple-instance learning approach similar to that proposed by Lu et al. . To extrAttention values per tile were stored and visualized using QuPath  to enablThe documentation of this work is compliant with the recommendations and guidelines for reporting minimum information about clinical artificial intelligence modeling (MI-CLAIM) as proposed by Norgeot et al. . InformaInformed consent was obtained from all participating subjects, and the experimental studies were authorized by the ethics committee of Justus Liebig University Giessen (AZ138/18 and 07/09).We assessed the performance of the AI-assisted morphological classification of methylation classes using balanced accuracy values, which also account for class imbalances present in the data distribution, thus giving more reliable results than simple accuracy values. Details on computational analyses can be found in the supplement. We conducted Monte Carlo cross-validation with 50 runs and obtained a 95% confidence interval for balanced accuracy of 0.896 ± 0.151 for the benign-1 vs. benign-2 combination and 0.830 ± 0.146 for benign-1 vs. intermediate-A. For benign-2 vs. intermediate-A, we were not able to distinguish the classes effectively, achieving a 95% confidence interval of 0.542 ± 0.176, suggesting results close to random guessing. In addition, the diagnostic performance for each pairwise classification is illustrated by computing the sensitivity vs. specificity as standard ROC curves. Mean areas under the curve (AUC) ± one standard deviation are provided for quantitative evaluation. Benign-1 vs. benign-2 (sensitivity for benign-2) with a mean AUC of 0.95 ± 0.04, and benign-1 vs. intermediate-A (sensitivity for intermediate-A) with a mean AUC of 0.90 ± 0.07 confirmed the high accuracy of our approach in discriminating benign-1 from either benign-2 or intermediate-A WSIs. In agreement with the low balanced accuracy value, benign-2 vs. intermediate-A classification had a mean AUC of 0.58 ± 0.11, indicating unsuccessful discrimination in our experiments.narrow validation according to Kleppe et al. [To further evaluate the diagnostic performance of our approach, we analyzed a second set of in-house patient data  that was not included in the previous dataset (Dataset A) used for training the deep-learning models. This experimental setup is considered a e et al.  The cohoWSIs often contain diverse non-tumor tissues such as hemorrhage, bone, brain tissue, or tissue artifacts that might compromise the diagnostic accuracy of the AI-assisted classification. In addition, tumor heterogeneity may also be associated with differences in the DNA methylome that are not fully captured and represented in the methylome profiling data. We, therefore, tested whether training on preselected tumor areas with available methylome data from the same region could consequently improve network performance. We trained our deep-learning model with Dataset C (which contained representative tumor sections selected by an experienced neuropathologist) and evaluated the model by narrow validation with Dataset D. In advance, we trained on Dataset C without restricting the tiles to the representative tumor sections to ensure that the change to Dataset C from Dataset A as in our previous results did not lead to unwanted side effects. We received a 95% confidence interval of 0.906 ± 0.143 (compared to 0.896 ± 0.151 from Dataset A) for benign-1 vs. benign-2, 0.852 ± 0.142 (compared to 0.830 ± 0.146 from Dataset A) for benign-1 vs. intermediate-A and 0.616 ± 0.199 (compared to 0.542 ± 0.176 from Dataset A) for benign-2 vs. Intermediate-A. This corroborates the comparable performance of Dataset C to Dataset A. For the benign-1 vs. benign-2 class combination in Dataset C, we achieved a 95% confidence interval for balanced accuracy of 0.828 ± 0.220, and for benign-1 vs. intermediate-A 0.810 ± 0.200. For benign-2 vs. intermediate-A, the results were close to random guessing with a balanced accuracy of 0.532 ± 0.215. ROC curves showed a mean AUC of 0.90 ± 0.09 for benign-1 vs. benign-2 (sensitivity for benign-2), a mean AUC of 0.86 ± 0.10 for benign-1 vs. intermediate-A (sensitivity for intermediate-A), and mean AUC of 0.56 ± 0.14 for benign-2 vs. intermediate-A (sensitivity for intermediate-A). In the narrow validation Dataset D, for the benign-1 vs. benign-2 combination, we achieved a 95% confidence interval for balanced accuracy of 0.813 ± 0.069, for benign-1 vs. intermediate-A 0.751 ± 0.072. For benign-2 vs. intermediate-A, the results were again close to random guessing with a balanced accuracy of 0.589 ± 0.134 a. ROC cuTo gain insight into the histological tumor areas that the attention network deems relevant for proper meningioma classification, we evaluated the attention maps generated from the attention network’s prediction. As attention maps provide a view into the model’s decision-making process, we evaluated them statistically beyond visual inspection. We used models trained on Dataset A as they performed better compared to Dataset C. To assess whether the attention network focuses on structures that an experienced neuropathologist would also consider relevant, we evaluated the proportion of high-attention tiles inside and outside areas that were highlighted as highly representative, using Dataset D. As additional evaluation revealed the higher importance of 20× magnification tiles, we focused on their attention maps. We calculated the proportion of high and low attention tiles inside and outside the marked area per patient and per run, averaging over both . In bothIn this study, we introduce an AI-assisted image analysis approach that leverages the visual decoding capabilities of convolutional neural networks (CNN) to identify prognostically relevant, methylome-defined tumor classes of meningiomas ,33 usingOur primary objective was to predict methylation classes in meningiomas using a deep-learning framework based on histological features—a task not achievable at the neuropathologist level—as DNA methylation-based molecular classification has demonstrated higher predictive power for tumor recurrence than histopathological classification alone . The evaOne limitation of our study is the small cohort size for some methylation classes, which precluded multi-class comparisons. This situation reflects the real-life clinical setting, as approximately 90% of patient samples in our retrospective dataset belong to only three methylation classes . Consequently, the other three methylation classes  were not included in the deep-learning framework due to their small cohort size. To enhance the applicability of our framework in a clinical setting, we are currently gathering samples for the underrepresented meningioma methylation classes to enable multi-class comparisons and incorporate additional molecular methylome-based meningioma classifications ,39,40. TBy showcasing the feasibility of predicting methylome-based tumor subtypes in meningiomas through computational approaches, our research contributes to the broader understanding of AI’s potential in tumor classification and diagnosis.Interestingly, training the network on a preselected and highly representative tumor area Dataset C—at the same time excluding non-tumor tissue, tissue artifacts, and potential tumor heterogeneity, which is not captured during DNA methylome profiling—did not improve its performance. In contrast, in Dataset A, the training on a higher amount of tiles—approximately by a factor of 10—and with higher variance in tissue diversity resulted in improved performance while allowing the network to ignore irrelevant tissue sections. This finding indicates that restricting the dataset to a smaller number of tiles and a reduced variance in relevant tissue/tumor areas during training (Dataset C) may slightly reduce the performance and weaken the generalization ability of the network. This is in concordance with previous findings  showing Overall, our study serves as an important proof-of-principle for integrating AI-assisted digital histopathology into existing diagnostic and prognostic methods for meningiomas and possibly other CNS tumors. We demonstrate the potential of CNNs to extract clinically relevant methylation patterns from histological features. As we continue to refine these techniques and expand their application to different molecular classifications, as discussed above, we can expand their utility and move closer to more accurate and clinically relevant meningioma stratification, ultimately improving patient care and outcomes. It will be interesting to see whether, in the future, these features can also be made apparent to the human (neuropathologist’s) eye. Our approach could expand the repertoire of existing methods for the diagnosis and prognosis of meningiomas, and possibly other tumors, in keeping with the idea put forth by Rudolf Virchow that “each anatomical change must have been preceded by a chemical one” .All computations were performed on a single Linux Ubuntu (version 20.04.3 LTS) Client with an Nvidia RTX3090 24 GB as a graphics card with CUDA (version 11.2) for network training and evaluation. The code is written in Python (version 3.8). We used OpenSlide  , and the best network according to validation loss is stored. Validation loss is computed on a random subset of the validation set with an equal number of patients per class per epoch.In each iteration, one patient with its entire set of tiles was used, and the attention part of the network was optimized using categorical cross-entropy loss and Adam  optimizeComputational analyses were performed in matplotlib , ptitpriAlthough methylation classes share similar clinical behavior and transcriptomic profiles, different CNS WHO grades are represented to varying degrees within each class. For instance, grades 2 and 3 are overrepresented in intermediate and malignant subclasses. The uneven distribution of tumor samples with different grades within individual methylation classes and the corresponding differences in tissue structure could serve as a distinguishing feature primarily in the pairwise classification of benign versus other methylation classes. This could potentially affect AI-based histopathologic classification. In our narrow validation Dataset B, the grade 1/grade 2 distributions are 12/1 for benign-1, 17/2 for benign-2, and 6/12 for the intermediate-A cohorts . The higWe evaluated the robustness of the attention network’s focus on different areas over multiple runs of the Monte Carlo cross-validation and assessed the proportions of high-attention tiles in 20× magnification vs. 40× magnification. We were able to observe that high and low attention tiles remained particularly important across runs, implying that the focus of the network and, therefore, the attention assignment remained fairly stable across multiple runs even with changing training data. Interestingly, the network seemed to focus more on 20× magnification tiles as their high-attention proportion increased compared to 40× magnification. Information from 20× magnification includes more global tissue structures, which seemed to be more relevant for the classification.


Moreover, The triple combination DOX/Hyd/Dis induced significant apoptosis in both MCF-7_WT and MCF-7_DoxR cells compared to DOX alone. The triple combination of DOX, Dis, and Hyd showed a synergistic drugs combination to decrease the DOX dose needed to kill both MCF-7_WT and MCF-7_DoxR cancer cells and enhanced chemosensitivity to DOX.Disulfiram and hydralazine have recently been reported to have anti-cancer action, and repositioned to be used as adjuvant in cancer therapy. Chemotherapy combined with other medications, such as those that affect the immune system or epigenetic cell profile, can overcome resistance with fewer adverse effects compared to chemotherapy alone. In the present study, a combination of doxorubicin (DOX) with hydrazine (Hyd) and disulfiram (Dis), as a triple treatment, was evaluated against wild-type and DOX-resistant MCF-7 breast cancer cell line. Both wild-type MCF-7 cell line (MCF-7_WT) and DOX-resistant MCF-7 cell line (MCF-7_DoxR) were treated with different combination ratios of DOX, Dis, and Hyd followed by measuring the cell viability using the MTT assay. Synergism was determined using a combination index, isobologram analysis, and dose-reducing index. The anti-proliferation activity and mechanism of the triple combination were investigated by apoptosis analysis. The results showed a reduction in the IC Breast cancer is one of the most common cancers affecting females . AlthougMany FDA approved drugs have an indication, such as hydralazine , were reDisulfiram (Tetra-ethyl-Disulfane dicarboamide) is an aldehyde dehydrogenase enzyme (ALDH) inhibitor that has been used for alcohol abuse for more than 60 years . ALDH isBased on Hyd and Dis capacity to exert anti-tumor activity and the superior efficacy of combination therapies against cancer, we combined DOX with Hyd and Dis  as a tri2 tissue culture incubator .The parental MCF-7 (breast cancer cell line) (MCF-7_WT) was obtained from ATCC, and DOX resistance MCF-7 cell line (MCF-7_DoxR) was developed from the parental cell line at Cell Therapy Center (The University of Jordan) . Cells w50 values were measured using MTT assay. Firstly, MCF-7_WT and MCF-7_DoxR cells (9 × 103 cells/well) were seeded into a 96-well plate  and treated with different concentrations of DOX, Dis, and Hyd ranging from 0.001 to 100 μM. Then, cells were incubated at 37°C in a 5% CO2 incubator for 72 h, after which the old media was aspirated, and 10 μl of MTT salt  in 100 μl of fresh media was added to each well. After that, plates were incubated at 37°C for 3 h, followed by adding 50 μl of solubilization solution (DMSO). The absorbance of the solution was measured at 560 nm using a Glomax plate reader .To determine the anti-proliferative effect of single and combinations of Doxorubicin, Disulfiram , and Hydralazine  on both cell lines, ICTo determine the effect of using combinations of DOX, Dis, and Hyd on MCF-7_ DoxR and their control cells MCF-7_WT, the combination index was calculated using Compusyn software . Briefly, an isobologram analysis was performed using the Compusyn software program. Cells were treated with different combinations of DOX, Dis, and Hyd either individually or in combinations of a 0.03:20.00:0.25 molar ratio, respectively, for 72 h followed by MTT assay analysis to determine the cell viability and combination index (CI). A CI of <1.0 indicates synergism, a CI of 1 indicates additive activity, and a CI > 1.0 indicates antagonism.A assessment of key regulatory molecules involved in inducing network interactions of the synergistic effect of Hyd, Dis, and DOX combination using the STRING database for prediction of the possible direct and indirect protein interactions .5 cells/well) were seeded into 12-well plates and exposed to 0.5 μM DOX, 0.5 μM Dis, and 50 μM Hyd. After 24 h, the cells were trypsinized using StemPro™ Accutase™ Cell Dissociation Reagent . Then, the collected cells were washed with PBS. After that, an Annexin V/PI apoptosis kit  was used to stain the cell pellets following the kit’s instructions. 10,000 events were counted by BD FACS CANTO II and analyzed using BD FACS Diva™ software version 7.0.To investigate the growth inhibition of all cell lines treated with disulfiram, hydralazine, and doxorubicin, the mechanism of cell death was determined by Annexin V/ Propidium iodide (PI) stain using flow cytometry. MCF-7_WT and MCF-7_DoxR cells  and activating hypoxia-inducible factor (HIF), hydralazine can potentially induce a hypoxic microenvironment within the tumor, which may sensitize cancer cells to the cytotoxic effects of DOX. Hypoxia has been linked to chemoresistance, and targeting the HIF pathway might help overcome this resistance .To analyze the protein interactions that are involved in increasing the chemosensitivity of cells to DOX, STRING modeling and simulation were used to understand the possible mechanism of synergism between the triple combination . The intWalhan et. al. investigated the effect of gene silencing on the expression of STAT3, NOCH1, and ATP-binding cassette (ABC) that were involved in drug resistance, and they revealed that all of them were downregulated due to gene suppression . Based oHydralazine as an inhibitor of DNA methylation, can potentially enhance the DNA damage caused by DOX and impair DNA repair mechanisms. This synergistic effect may potentiate the cytotoxicity of DOX. Furthermore, combined treatment with hydralazine and DOX may enhance the suppression of Notch signaling cell sensitivity to DOX , 39. DoxRegarding cell cycle protein interactions, the cell cycle is a highly regulated process that involves the coordinated action of various proteins to ensure accurate cell division. Multiple regulatory steps are involved in the progression of the cell cycle. Disulfiram, on the other hand, can inhibit DNA repair enzymes such as aldehyde dehydrogenase (ALDH), which is involved in DNA base excision repair. The combination of these drugs can impair DNA repair pathways  27], ac, ac27], Breast cancer is the most diagnosed cancer and is associated with high mortality rates worldwide . Doxorub50 of Hyd, Dis, and the combination was found to be 165.1, 0.73, and 20.03 μM, respectively (50 of Dis from 0.73 μM to 0.03 μM and Hyd from 165.1 to 20 μM (The effect of Dis/Hyd combination was investigated to confirm their anti-cancer activity and synergism toward wild-type MCF-7 breast cancer. Cell viability (MTT) assay was performed to explore the effect of Dis and Hyd on cell growth. MCF-7 cells were treated with increasing concentrations of both drugs separately and combined. The ICectively . Findingto 20 μM .50 values.The combination of Dis and Hyd showed synergism with a combination index (CI) < 1 at Fa ranging from 0.4% to 0.95% . Also, iThe mechanism of cell death was determined by investigating the effect of Dis/Hyd combination on apoptosis. Combining the two drugs enhanced apoptosis compared to either Hyd or Dis on MCF-7_DoxR cells but not on MCF-7_WT cells . There wFollowing the successful confirmation of synergism between Dis/Hyd combinations, the effect of the triple drug combination (DOX/Dis/Hyd) on cell growth inhibition was explored on MCF-7_WT and MCF-7_DoxR cells.Combining drugs to treat cancer is an effective strategy to overcome drug resistance, especially if the combined drugs are relatively safe and show synergistic effects . This st50 the concentration of DOX decreased from 0.24 to 0.012 μM in MCF-7_WT cells (MCF-7_WT and MCF-7_DoxR cells were treated with a combination of (Dis 0.03 μM and Hyd 20 μM) and different concentrations of doxorubicin, and the results of the three drugs are shown in Figs WT cells .50 of DOX in the resistance cells decreased from 1.13 μM to 0.44 μM (50 of wild type compared to resistant cells (20-fold vs 2.7- fold) can be explained by the presence of other important signalling pathways that are pivotal in resistant cells due to their unique characteristics.The three combined drugs were investigated on (180 nM) MCF-7_DoxR cell line. The cytotoxic effect of DOX combined with Dis (0.03μM) and Hyd (20μM) was found to be higher than DOX alone in resistance cells . The IC5 0.44 μM . The dif95) reduced eight-folds after combination with renieramycin [Any breakthrough that enhances the sensitivity of resistant cells to chemotherapy has the potential to significantly impact cancer treatment and patient outcomes. Understanding the physiological relevance of increasing sensitivity to chemotherapy in resistant cells could potentially contribute to the development of personalized treatment strategies , 58. By eramycin .It was reported that Dis synergizes DOX effect on cell growth of triple-negative breast cancer , hepatocThe effect of DOX/Hyd/Dis combination on the mechanism of cell death was determined using an apoptosis assay. The triplet combination DOX/Hyd/Dis induced statistically significant apoptosis in both wild type and resistance compared to DOX alone, 79.5% compared to 63.05% in the wild type and 71.6% compared to 61.35% in the resistant cells, p-value = 0.002 and 0.03, respectively .in vitro. This triple combination might offer new methods of improving treatment therapy of doxorubicin in resistant tumor cells and reduce the dose of the chemotherapeutic drug. Future work will involve combinations of these two promising anti-cancer drugs with other drugs with well-known safety profiles to provide new strategies to combat chemotherapy resistance. Using a Xenograft experiment of human breast cancer will provide more clarity regarding the therapeutic efficacy of the triple (DOX/Dis/Hyd) combination. It’s important to note that the use of disulfiram and hydralazine in cancer treatment is still under investigation, and their combined mechanism of action may vary depending on the specific cancer type and context. Clinical trials and further research are necessary to fully understand and optimize the potential synergistic effects of this drug combination. Clinical trials and further research are necessary to fully understand and optimize the potential synergistic effects of this drug combination.In conclusion, this study showed for the first time a synergetic effect of DOX/Dis/Hyd combination against MCF-7 breast cancer cells proliferation


Burkholderia pseudomallei, but few studies have identified promising biomarker candidates to predict outcome.Melioidosis is an often-fatal tropical infectious disease caused by the Gram-negative bacillus In 78 prospectively enrolled patients hospitalized with melioidosis, six candidate protein biomarkers, identified from the literature, were measured in plasma at enrollment. A multi-biomarker model was developed using least absolute shrinkage and selection operator (LASSO) regression, and mortality discrimination was compared to a clinical variable model by receiver operating characteristic curve analysis. Mortality prediction was confirmed in an external validation set of 191 prospectively enrolled patients hospitalized with melioidosis.p < 0.01, p = 0.03, respectively). In the external validation set, the IL-1R2 + sTREM-1 model  had superior 28-day mortality discrimination compared to a modified SOFA model  and was similar to a model containing IL-1R2 alone .LASSO regression selected IL-1R2 and soluble triggering receptor on myeloid cells 1 (sTREM-1) for inclusion in the candidate biomarker model. The areas under the receiver operating characteristic curve (AUC) for mortality discrimination for the IL-1R2 + sTREM-1 model  as well as for an IL-1R2-only model  were higher than for a model based on a modified Sequential Organ Failure Assessment (SOFA) score (AUC 0.69, 95% CI 0.56–0.81, Biomarker models containing IL-1R2 had improved 28-day mortality prediction compared to clinical variable models in melioidosis and may be targets for future, rapid test development. Burkholderia pseudomallei. Melioidosis is endemic to parts of Southeast Asia, South Asia, Northern Australia with sporadic cases reported in the Americas  and the Acute Physiology and Chronic Health Evaluation (APACHE) III score–reasonably predict mortality in patients with sepsis in Southeast Asia; however, these approaches require extensive clinical and laboratory data, limiting their utility in low- and middle-income countries (LMIC) where many melioidosis cases may occur –6. Many In the last several years, commercially-available tests have been developed for the rapid, bedside diagnosis of melioidosis . AdditioB. pseudomallei culture positivity. This cohort has previously been described in part  as well as S100A9 (Biolegend). In the external validation cohort, plasma concentrations of IL-1R2 (Abcam) and sTREM-1 (R&D Systems) were measured by ELISA. Samples were tested and analyzed per the manufacturers’ instructions.The SOFA score was originally designed as a sequential scoring system using clinical and laboratory data as markers of organ dysfunction and/or failure, including respiratory, coagulation, hepatic, cardiovascular and renal components . In the In the external validation cohort, a modified SOFA score including the Glasgow Coma Scale (GCS) was calculated by adding the highest component score from the time of presentation to the time of study enrollment, which was within 24 h of admission. Because arterial blood gases are not frequently used at the study hospital and dosages of vasoactive medications were not recorded, the cardiovascular and respiratory components of the SOFA score were modified as described above and as previously reported .Novel candidate models of mortality prediction were developed according to Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) recommendations . To deveVariables were reported as median and interquartile range (IQR) for non-normally distributed data. Differences in biomarker concentrations were evaluated by the Mann–Whitney U test. Biomarkers were log10-transformed prior to regression analysis and the association of selected biomarkers with 28-day mortality was determined by logistic regression.Correlations between biomarkers were assessed using a Spearman’s rank-order correlation. Mortality prediction models of clinical variables were generated using the cohort-specific organ failure score or modified SOFA score. Analyzes were performed using Stata SE version 14.2 .Plasma samples were obtained at study enrollment from patients with culture-proven melioidosis in the derivation cohort. The study flow diagram is shown in p < 0.01; p < 0.001; Plasma concentrations of IL-1R2, CRP, PCT, sTREM-1, S100A9 and calprotectin were measured at the time of study enrollment and were compared between survivors and non-survivors at 28-days. Concentrations of IL-1R2 and PCT were significantly higher in non-survivors compared to survivors (both p = 0.53) and so both biomarkers were carried forward for further evaluation.To develop the simplest yet unbiased mortality prediction model, we performed LASSO regression on all six measured biomarkers from the derivation cohort, regardless of association with 28-day mortality. LASSO regression selected IL-1R2 and sTREM-1 for the final model. IL-1R2 and sTREM-1 were not significantly correlated . Additionally, the IL-1R2 model had significantly higher mortality discrimination compared to the organ failure score model  and similar discrimination compared to the two-biomarker model (p = 0.21). All models maintained their mortality discrimination after optimism-adjustment by bootstrapping and were assessed for calibration by goodness-of-fit testing  was calculated for both models as well as single biomarker models containing IL-1R2 and sTREM-1 . The two testing .p = 0.02; p = 0.05). Given these findings, both models were brought forward for validation in an external cohort.Given the similar mortality discrimination of the two-biomarker model and the IL-1R2 model, we next tested whether the IL-1R2 model had improved performance after the addition of sTREM-1. The addition of sTREM-1 did significantly improve the IL-1R2 model (likelihood ratio (LR) p < 0.001; p < 0.001; p < 0.01; p = 0.06) and was similar to the two-biomarker model (p = 0.33). Both models had unchanged discrimination after bias-adjustment and appropriate goodness-of-fit  and specificity of 85%  as the primary outcome, important in areas such as northeast Thailand where many critically ill patients are discharged at their request to die at home.Our study has a variety of strengths. This is one of the few reported studies to include prospectively enrolled patients with melioidosis in independent cohorts from different sites. All included subjects had culture-proven a priori to select the simplest biomarker model while minimizing bias. However, other biomarkers such as CRP and PCT, which were not carried forward in our selection process, may furnish similar mortality discrimination to those analyzed. Finally, outside a randomized controlled trial, assessing the clinical utility of a biomarker model is challenging , Udon Thani Hospital Ethics Committee (00.32.102/318), Sunpasitthiprasong Hospital Ethics Committee (039/2556), the University of Washington Institutional Review Board (42988) and the Oxford University Tropical Research Ethics Committee (OXTREC172-12). Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.TK, NC, SW, and TW were involved in the design of the study. TK, TY, SW, LL-M, GR, DD, and AD collected samples and performed experiments. RP, VH, and GL recruited patients and collected data. TW, SW, and TK performed the statistical analysis. ND, NC, DL, and GL provided facilities. NC and TW were responsible for supervision of the study. All authors were involved in writing the paper and have approved the final version.This work was supported by the United States National Institutes of Health  and the Wellcome Trust . The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Wellcome Trust. For the purpose of Open Access, the authors have applied a CC BY public copyright license to any author accepted manuscript version arising from this submission.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


With the emergence of SARS-CoV-2 mutant strains, especially the epidemic of Omicron, it continues to evolve to strengthen immune evasion. Omicron BQ. 1 and XBB pose a serious threat to the current COVID-19 vaccine  and COVID-19-positive survivors, and all current therapeutic monoclonal antibodies are ineffective against them. Older people, those with multimorbidity, and those with specific underlying health conditions remain at increased risk of COVID-19 hospitalization and death after the initial vaccine booster. However, small-molecule drugs for conserved targets remain effective and urgently needed.The non-structural protein of SARS-CoV-2 non-structural protein 1(Nsp1) can bind to the host 40S ribosomal subunit and activate the nuclease to hydrolyze the host RNA, while the viral RNA is unaffected, thus hijacking the host system. First, the present study analyzed mutations in the Nsp1 protein and then constructed a maximum-likelihood phylogenetic tree. A virtual drug screening method based on the Nsp1 structure (Protein Data Bank ID: 7K5I) was constructed, 7495 compounds from three databases were collected for molecular docking and virtual screening, and the binding free energy was calculated by the MM/GBSA method.Our study shows that Nsp1 is relatively conserved and can be used as a comparatively fixed drug target and that therapies against Nsp1 will target all of these variants. Golvatinib, Gliquidone, and Dihydroergotamine were superior to other compounds in the crystal structure of binding conformation and free energy. All effectively interfered with Nsp1 binding to 40S protein, confirming the potential inhibitory effect of these three compounds on SARS-CoV-2.In particular, Golwatinib provides a candidate for treatment and prophylaxis in elderly patients with Omicjon, suggesting further evaluation of the anti-SARS-CoV-2 activity of these compounds in cell culture. Further studies are needed to determine the utility of this finding through prospective clinical trials and identify other meaningful drug combinations. Since November 9, 2021, a new novel coronavirus variant has been detected in South Africa, and more than 300 variants of subtype Omicron have appeared. Novel coronavirus variant has caused several outbreaks worldwide and continued to evolve toward strengthening immune evasion. Major epidemic strains in many countries worldwide have been replaced by BQ. 1 and XBB families. XBB mutant strain was first discovered in India in August 2022. It spread rapidly and soon became a major epidemic strain in Singapore and other countries. Novel Coronavirus Omicron new subtype BQ. 1.1 is resistant to all currently known monoclonal antibody therapies . AccordiHowever, the Omicron BA.5 subclade BA.5.2 and BF.7 are still the most prevalent in China, indicating that herd immunity is weak for new mutants that evade the immune barrier established by prior vaccination and natural infection, and mRNA vaccines, similar to inactivated vaccines, have a limited role in preventing XBB and BQ. 1.1 breakthrough infection. The rise of these new variants is mainly attributed to mutations in the receptor binding domain (RBD) of the viral spike protein, which helps novel coronavirus evade the COVID-19 vaccine and neutralizing antibodies induced by natural infection. The more RBD mutations, the faster the mutant strain seems to rise. BQ. 1.1 has six RBD mutations, one more critical mutation than BQ. 1, while the RBD mutations in XBB have seven. These new RBD mutations in the Omicron mutant strains have a convergence effect. Overall, RBD has 7–10 identical mutation sites, which were not present before the COVID-19 Outbreak, demonstrating that the convergent evolution mutation sites have a high selection advantage and that there is a great deal of concentrated evolutionary pressure, which will eventually result in convergence evolution and evolution . BecauseVaccination has been widely promoted as an important preventive measure against COVID-19. Among clinically developed vaccines, protein subunit (32%), RNA (23%), viral vector (non-replication) (13%), inactivated virus (13%), DNA (9%), and other types of vaccines. As research on protein subunit vaccines was relatively mature and was the priority vaccine development method, the number of protein subunit vaccines was the largest among COVID-19 vaccines. However, persistent mutations of the virus can affect the vaccine’s preventive effect, especially Omicron, which largely evaded the antibodies elicited by the vaccine .SARS-CoV-2 comprises a single-stranded positive-sense RNA genome that encodes structural and non-structural proteins. Nsp1 is one of the first proteins expressed from the SARS-CoV-2 genome and is a major virulence factor for COVID-19. Studies on SARS-CoV-2 have implicated Nsp1, the first encoded viral protein, as a virulence factor with a key role in the host translation shutdown. The presence of Nsp1 in ribosomal preinitiation complexes very likely prevents the accommodation of mRNA into the entry channel of the 40S subunit to inhibit translation initiation . These pDeveloping a new drug is time-consuming and requires a huge financial investment. In the current global crisis, repositioning existing drugs is a potentially useful tool for finding new treatment options. Computer-assisted virtual screening provides an inexpensive and rapid alternative to high-throughput screening for drug discovery. Furthermore, virtual screening techniques can optimize the selection of potential drugs. Over the past few decades, virtual screening has played an important role in discovering small-molecule inhibitors of therapeutic targets. Various ligands, and structure-based virtual screening methods, have been used to identify small molecule ligands for a protein of interest .Our study first analyzed the amino acid sequence mutations of the Nsp1 protein to construct a maximum-likelihood phylogenetic tree. The competitive binding of small molecules of the potential Nsp1-40S complex was investigated using docking-based virtual screening methods. We used molecular docking and molecular dynamics (MD) simulations for Nsp1 and the compound. Compounds were classified according to the combined free energy score. Potential drug compounds that could inhibit Nsp1 were identified by analyzing the binding patterns between the compounds with better scoring results and Nsp1. In parallel, we performed binding free energy calculations for complexes of eight compounds and Nsp1. This study aimed to identify potential drug compounds in the drug library by molecular docking and Nsp1 simulation, which may shed insights for future studies on small molecule medications that can be used to treat novel coronavirus infections clinically.https://www.ncbi.nlm.nih.gov/sars-cov-2/),The Nsp1 sequences were extracted by a custom python script. The MAFFT software (ref. 1) was applied to perform a multiple sequence alignment analysis for amino acid mutations  was used to make the re-root tree. The R package ggtree (ref. 3) was used to visualize the tree (The IQTree software (ref. 2) was used to build the maximum-likelihood phylogenetic tree . The Figthe tree .-Nsp1 complex and default values are used for parameter setting.Homology model of the SARS-CoV-2-Nsp1(BQ.1) protein was built by modeller9.18 using the crystal structure of 40S-SARS-CoV-2-Nsp1 (PDB: 7K5I) as a template. Hundred independent structures were constructed, and the one with the best DOPE score was chosen for further energy minimization in Amber18 using an ffff14SB force field. Rosetta3.7 was used to perform protein-protein docking to get the 40S- BQ.1https://zinc.docking.org/). Virtual screening of the small molecule database was conducted using autodock-vina.22. Using prepare_ligand4.py scripts, small molecule mol2 files were converted to pdbqt format files and kept all rotatable keys flexible. Using the prepare_receptor4.py script, the receptor protein pdb file was converted to pdbqt files, which was used as a molecular pair acceptor. The molecular docking parameters are as follows: center_x = 4.87, center_y = -16.03, center_z = 8.314, size_x = 40, size_y = 40, size_z = 40, exhaustiveness = 32. Each small molecule will produce the affinity data. This data was sorted and filtered.Each sub-library  was downloaded from the zinc database  15 and Amber ff14SB force fields, respectively. 10,000 steps of constraint minimization , including 5000 steps of steepest descent minimization and 5000 steps of conjugate gradient minimization to optimize each system. Each system was then heated to 300k in 0.2 ns and equilibrated for 0.1 ns in the NPT ensemble. Finally, a 5ns MD simulation was performed for each system at 300k. Minimization, heating, and equilibration were performed using the sander program in Amber18. A 5-ns production run was performed using the pmemd.cuda. Based on the 5 ns MD simulation trajectory, the binding free energy (ΔG) was calculated by the MM/GBSA method with the following formula: ΔGcal=ΔEvdw+ΔEele+ΔGgb+ΔGnp-TΔS, where ΔEele and ΔEvdw refer to electrostatic and van der Waals energy terms respectively. ΔGgb and ΔGnp refer to polar and non-polar solvation-free energies, respectively.The 40S-SARS-CoV-2-Nsp1 (BQ.1) crystal structure was used as a template to model the SARS-CoV-2 target 40S using modeler 9.18. One hundred independent structures were constructed, and the structures with the best DOPE score were selected to reduce the energy of Amber further. The relaxed model was saved as pdb files and converted to pdbqt format as a pair acceptor using AutoDockTools-1.5.6 with specified atomic type and partial charge. The docked minimal conformation was used as the initial position of the drug molecule. Each simulation system was immersed in a cubic cube of TIP 3 P water at a distance of 10 A from the solute. To neutralize the system with either NaTo analyze the mutation in the Nsp1 amino acid sequence, we selected 23 currently mainstream Omicron mutant strains for analysis, and the sequence of YP _ 009742608.1 was used as a control. All VOC has a common S135R mutation, and secondly, BJ. 1 and XBB have two common mutations: K47R and G82D. The strain BM.1.1.1 has amino acid mutations at two sites: R77Q and K120N. The results show that the sequence of Nsp1 is relatively conserved. The results are shown in The 7,495 drugs obtained from the zinc database were screened for molecular docking. Among them, 93 small molecule compounds have docking scores better than 7.5 kcal/mol. 24 of the 2,100 compounds approved by the FDA. There were 4,263 compounds approved by regulatory agencies other than the FDA. Among them, 25 compounds with a docking score better than -7.5 kcal/mol. 34 of the 1,132 compounds in clinical trials have a docking score better than -7.5 kcal/mol. In addition, 21 top compounds with docking scores better than -8 kcal/mol were selected from homologous model docking results Table 1.Paxlovid is already diagnosed with coronavirus infection treatment, which is unsuitable for prevention. The national diagnosis and treatment plan is light or common type have severe high-risk factors of adults, no high-risk factors of no need to use, need to use within 5 d, more than 5 d is affected by obvious effect, the note is mainly and many drugs conflict, need to inform the doctor at ordinary times, evaluated by the doctor whether can use Paxlovid, basic medication may need to adjust first. Therefore, medication has significant limitations .These COVID-19-infected patients need antiviral drugs or other drugs to treat SARS-CoV-2 infection. More potential compounds need to be tested. In the presence of 40S-SARS-CoV-2-Nsp1 (BQ.1) complex formation, that is, when symptoms of infection may already be present, small molecule interference with the 40S-SARS-CoV-2- Nsp1 (BQ.1) complex can be assessed by assessing the likelihood of small molecules entering the lumen of the complex. Further, eight top compounds that showed a docking score better than 8 kcal/mol were selected from the docking results of the homology model Figure 3Cepharanthine has been successfully applied to treat many diseases, including tuberculosis, pneumoconiosis, AIDS, and SARS . StudiesHypericin Inhibits the protein kinase activity required for many viruses’ replication. Hypericin induces significant changes in the HIV capsid protein p24 in the presence of light and may suppress reverse transcriptase activity .in vitro inhibition experiments. Related functional experiments proved that the small molecule does not cause the stability of HAV particles but also suppresses HAV infection by blocking the binding of HAV to cell surface receptors. Through in vitro inhibition of Golvatinib, Golvatinib (IC50 about 1uM) was selected as the lead drug with the best inhibitory effect. Relevant functional experiments showed that the small molecule did not cause changes in the stability of HAV particles but inhibited HAV infection by blocking the binding of HAV to cell surface receptors (in vitro assay revealed that HGF/MET inhibition, induced by golvatinib, constrained the growth of chemoresistant SCLC cells through the inhibition of ERK and AKT signals. Furthermore, treatment with golvatinib suppressed the systemic metastasis of SBC-5 cell tumors in natural killer cell-depleted SCID mice, predominantly through cell cycle arrest (Golvatinib is a lead drug with the best effect on HAV inhibition screened through eceptors . A cell-e arrest .in vivo HBV model opens a way to reduce the dose-limiting toxicities previously reported in Smac-mimetic clinical trials for HBV infection (Some studies have shown that combination of birinapant plus the MDR1 inhibitor zosuquidar is a safe and targeted therapy to kill HBV-replicating hepatocytes. Moreover, the observation that zosuquidar can safely potentiate the antiviral effect of birinapant in an nfection .Gliquidone, the second generation of oral sulfonylureas, a highly active pro-islet β -cell agent, combined with specific receptors on the islet β cell membrane, which can induce an appropriate amount of insulin to reduce blood glucose concentration. It has also been shown that Gliquidone can bind to the Mpro-binding pocket, suggesting that it may inhibit SARS-CoV-2 replication and transcription .Dihydroergotamine is a mixture of semi-synthetic ergot alkaloids mainly used for age-related cognitive impairment. This drug is used for the elderly with degenerative brain circulation disorders, senile dementia, cerebral arteriosclerosis, and stroke sequelae caused by dizziness, headache, attention loss, memory loss, depression, and fatigue . Dihydroin vitro (The binding free energy for the interface of 40S and Nsp1 of eight compounds was calculated. We calculated the binding free energy of five drugs by MM/GBSA through the simulation trajectory of 100 ns molecular dynamics simulations methods. Golvatinib has the strongest binding free energy, suggesting it can be tested their anti-SARS-CoV-2 infection in vitro Table 2.Although the pathogenicity of Omicron decreases, it will still cause death and the risk of sequelae in high-risk groups. Therefore, we still need to continuously monitor the changes of Omicron and possible variants in terms of pathogenicity, severe rate, and risk of sequelae. Although therapies and vaccines have been developed to promote immune protection against infection, several SARS-CoV-2 variants can evade current monoclonal antibody therapies . This vaThe original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.MS and SD designed the study. MS performed most studies, but a few were carried out by GL, TD and PD. All authors contributed thoughts and advice. All authors contributed to the article and approved the submitted version.


Glycine max Merr. (GM) is a functional food that provides many beneficial phytochemicals. However, scientific evidence of its antidepressive and sedative activities is scarce. The present study was designed to investigate the antidepressive and calmative effects of GM and its biologically active compound, genistein (GE), using electroencephalography (EEG) analysis in an electric foot shock (EFS)-stressed rat. The underlying neural mechanisms of their beneficial effects were determined by assessing corticotropin-releasing factor (CRF), serotonin (5-HT), and c-Fos immunoreactivity in the brain using immunohistochemical methods. In addition, the 5-HT2C receptor binding assay was performed because it is considered a major target of antidepressants and sleep aids. In the binding assay, GM displayed binding affinity to the 5-HT2C receptor . GE exhibited concentration-dependent binding affinity, resulting in the binding of GE to the 5-HT2C receptor . Administration of GM (400 mg/kg) increased non-rapid eye movement (NREM) sleep time. Administration of GE (30 mg/kg) decreased wake time and increased rapid eye movement (REM) and NREM sleep in EPS-stressed rats. In addition, treatment with GM and GE significantly decreased c-Fos and CRF expression in the paraventricular nucleus (PVN) and increased 5-HT levels in the dorsal raphe in the brain. Overall, these results suggest that GM and GE have antidepressant-like effects and are effective in sleep maintenance. These results will benefit researchers in developing alternatives to decrease depression and prevent sleep disorders. Sleep is essential for sustaining human health and well-being, and disturbances in sleep can negatively affect both physical and psychological health. Disturbance of sleep leads to problems with homeostatic control and elevates weakness against inflammation-related and chronic diseases . BecauseDuring initial sleep, HPA axis activity is repressed without interruption. In the latter half of sleep, HPA secretory activity boosts, almost reaching the upper limit for the circadian rhythm instantly upon waking up, and the important activity of the HPA axis and sympathetic nervous system has an effect on the amount of rapid eye movement (REM) sleep . The facStress is well known to be a common cause of short-term insomnia and induce sleep problems which are characteristics in the pathophysiology of stress-related disorders, such as depression, anxiety, or post-traumatic stress disorder (PTSD). Electric foot-shock (EFS) stress is utilized as a typical animal model which mimics the symptomology of stress-related disorders. It has been shown that exposure to electric shock reliably reduces both general and REM sleep, and foot shock is known as a more valid model of sleep changes in PTSD than immobilization stress ,6. RecenConsiderable evidence indicates the presence of brain 5-HT dysfunction in major depression  and sleeThe molecular targets for calmative effect primarily focus on modulating the serotonergic system, which is implicated in various mental functions related to relaxation, stress, and sleep regulation ,10,11. TGlycine max Merr. (GM) is a singular food source because of its rich nutrient content, including carbohydrates, proteins, isoflavones, minerals, and dietary fiber. GM intake has been linked to the prevention of diabetes mellitus, osteoporosis, skin disorders, and cancer. The health benefits of GM are attributed to the presence of bioactive compounds, which can be categorized into nonprotein compounds, such as isoflavones and saponins, and protein compounds. These compounds play a key role in several health effects owing to their ability to bind to estrogen receptors. Likewise, isoflavones from GM, such as genistein (GE), daidzein, and glycitein, have also shown immunomodulatory activity. GE, the most prominent component of GM isoflavones, possesses proverbial preventive activities against obesity, cardiovascular diseases, cancer, and liver injury. In addition, it may alleviate symptoms of menopause. However, no studies have examined the effect of GM and GE on sleep, and no conclusive mechanism for the clinical cure or sleep enhancement and associated disorders has been explicated.The present study assessed the potential of GM extract and GE as a new treatment to reduce somnipathy in the EFS-stressed animal model in rats. The study aimed to demonstrate the effects of GM extract and GE on sleep and provide a basis for further research on the underlying mechanisms connected with a potential GM extract cure.The binding activity of GM to the 5-HT2C receptor was displayed as 5-HT2C agonist binding. We found that GM displayed effective binding activity . The inhp < 0.001, p < 0.01, p < 0.05, p < 0.05, p < 0.05, The control group had a significantly increased wake time (81.57 ± 1.49) and decreased REM (6.67 ± 0.63) and non-rapid eye movement (NREM) (11.75 ± 1.57) compared to the normal group . We observed that EFS had negative effects on wake time  = 8.732, p < 0.001, p < 0.05, p < 0.01, respectively, p < 0.001, p < 0.05, p < 0.01, respectively, The expressions of CRF and c-Fos in the brain were assessed in all four groups . Previoup < 0.001, p < 0.001, The level of 5-HT in the brain was assessed in the GM and GE groups . The GM Several regions of the brain are involved in modulating sleep. Monoaminergic neurons, in particular, are involved in the conversion of the sleep–wake cycle and the balance of sleep patterns in various regions of the brain . The PVNA receptor-binding assay is an important tool in the search for drug candidates. In particular, the 5-HT2C receptor binding assay has been extensively used to screen for calmative and anxiolytic activities. GM displayed a moderate dose-dependent binding affinity to the 5-HT2C receptor . This result indicates that GM contained natural ligand-binding affinity to the 5-HT2C receptor.The present study evaluated the effects of GM and its bioactive component GE on sleep enhancement, induction, and preservation of stress-induced sleep interferences. We found that GM and GE reduced the wake ratio by sustaining a balance between sleep patterns and influencing the expression of PVN c-Fos and CRF in EFS-induced sleep disturbance. Additionally, GM and GE significantly increased REM time. GM and GE decreased CORT serum concentrations and maintained better sleep by increasing 5-HT immunoreactivity in the dorsal raphe in an ESP-induced sleep model.Foot-shock stress is widely utilized as a model to mimic the symptomology of stress-related disorders in animals. Previous studies have shown that exposure to electric shock reliably reduces general and REM sleep. We confirmed that EFS significantly increased total awake time, and reduced REM and NREM time, as seen in We identified GE as a bioactive constituent of GM and confirmed its effects on sleep enhancement and maintenance. In this study, GE showed similar effects to GM in the EFS-induced sleep disorder animal model. These results suggest that GM extract and GE may be effective in sleep maintenance and enhancement.Stress is a common cause of short-term insomnia, and the close relationship between sleep and stress has recently been identified. Sleep is commonly evaluated in many studies by exposing animals to foot-shock parameters planned to induce stress. GM and GE treatment after EFS exposure decreases serum corticosterone levels, the amount of c-Fos- and CRF-immunoreactivity in the PVN, and the level of 5-HT in the DRN. Our results indicate that GM and GE can mitigate the stress that causes disturbances of sleep.Changing electroencephalogram (EEG) frequencies help estimate sleep–wake states. During wake time, the EEG recordings go up and down by high-frequency and low-amplitude EEG. The power of EEG is considerably decreased in the low-frequency δ-wave and increased in the θ-wave range during REM sleep. NREM sleep is scored based on the presence of spindles interspersed with slow waves. In the sleep pattern of rats exposed to EFS, wake times increase and REM sleep time decreases until 12 h after sleep. Similarly, in this study, we induced EFS-related sleep disturbances by increasing wake times and decreasing REM sleep times. Our findings suggest that GM and GE stabilize sleep patterns by restoring wakefulness and REM sleep ratios.Previous studies have suggested that treatment with herbal medicine, which exhibits hypnotic effects, altered total sleep time and NREM rather than REM sleep . With thWe found enough evidence that GM and GE enhance stress and quality of sleep by studying some parameters, such as EEG, PVN c-Fos, CRF immunoreactivity, and DRN 5-HT, in an animal model of EFS-induced sleep. GM affected the 5-HT expression and the number of c-Fos- and CRF-immunoreactive neurons and restored NREM sleep patterns and CORT levels, which as a collective acted to reduce stress and is the major cause of somnipathy. GE had a particularly significant effect on wake times, REM, NREM, 5-HT expression, and the number of c-Fos- and CRF-positive cells in the PVN, thus supporting the action of GM on sleep improvement.Based on the sleep enhancement effects of GM and GE in animals, we intend to introduce GM and GE as treatments or supplements for sleep disturbance. In this study, to verify the effects of GM on sleep in an animal model, representative parameters, such as EEG, c-Fos, 5-HT, and CRF, were analyzed. In the model of EFS-induced sleep disturbance, the effectiveness of GM and GE was confirmed for Wake/REM sleep cycles and c-Fos and CRF levels were decreased. In addition, 5-HT expression levels were increased. The present study demonstrated that GE binds to 5-HT2C receptors and synergistically stimulates serotonergic neurons in the DRN. The serotonergic systems may be heavily involved in modulating the sedative or anxiolytic-like effects of GE. In addition to these studies, we also showed that GE reduced wake time and promoted significant REM and NREM sleep through the serotonergic system. These results suggest that GE produces its hypnotic and anxiolytic-like effects through the interaction of the 5-HT2c receptors, which are known to play an important role in sleep functions.Overall, these results suggest that GM and GE have antidepressant-like effects and are effective in sleep maintenance. These results will benefit researchers in developing alternatives to prevent depression and sleep disorders.Soybeans  were powdered and made circumfluent with 30% ethanol (3 times the weight of the beans) for 3 h at 90 °C. The ethanol supernatants were percolated and lyophilized to yield the final extract . GE was supplied by Sigma–Aldrich. .v/v) were used. Thereafter, 10 μL of the sample was injected.High-performance liquid chromatography (HPLC) was performed using Agilent 1100 series G1379A Degasser, G1312A Bin Pump, G1313A ALS, G1316 column, and G1313A VWD . As the HPLC column, a Luna  column was used, and the wavelength of the ultraviolet absorption detector was 254 nm. As the moving phase, an ammonium sulfate solution (1 → 100) and acetonitrile mixture solution  was labeled with Cy5  and free dyes were abandoned by Spin column . To make 5-HT2c receptor microarray, a glass slide covered with super epoxy group  was used. The antibody microarray using the glass chip was prepared by immobilizing 42 antibodies against cell cycle proteins. Cy5-labeled tryptamine and GM were applied to the Protein Chip and incubated for 1 h at 37 ◦C. The Protein Chip was then washed with PBST and DW and dried under a stream of N2 gas. GM was dissolved in ethanol and adulterated to the desired concentration using PBS. The GM concentrations ranged from 1000 µM to 15.625 µg/mL. Tryptamine alone was used as the negative control. GM was used to estimate its calmative effect using a 5-HT2c receptor binding assay. The chips were scanned using a GenePix 4100 A microarray scanner . The internally normalized proportion of all spots was evaluated.Eight-week-old SD rats from Samtako Animal Co.  were used for this experiment. The system automatically maintained proper temperature (20–25 °C), humidity (45–65%), and lighting (12:12 h light/dark cycle). Food and water were supplied ad libitum. All animal experiments were approved by the Institutional Animal Care and Use Committee of Kyunghee University Health System (KHUASP (SE)-14-051).The rats were split into four groups of six rats each, according to the EFS-induced sleep disorder model. The four groups were as follows: normal (“nor”: no stress), stress/control group , “GM” (400 mg/kg of GM extract with EFS), and “GE” (30 mg/kg of GE with EFS).After electroencephalogram (EEG) operation, the animals received EFS once a day for 5 days. EFS was administered in accordance with the following conditions at random: frequency = 10 times in 3 s; intensity = 3 mA; duration = 5 min.Electrodes of EEG were transplanted for polygraphic recording as described in the stereotaxic atlas of Paxinos and Watson . AnestheAnimals were habituated to the recording conditions prior to the test after the operation. GM and GE were separately dissolved in 0.9% saline solutions  and administered orally for 5 days, as well as administering EFS stress before EEG recording. Treatment with saline and bean GE was performed 10 min before the EEG recording. After treatment, animals were immediately connected to EEG recording cable (two EEG channels). The software indicates wakefulness as high-frequency low-amplitude EEG, and NREM was scored on the presence of spindles scattered with slow waves in the EEG. EEG power during REM was significantly decreased in lower-frequency δ-wave (0.75–4 Hz) and increased in the range of θ-wave activity .Rats were habituated to the recording conditions prior to the test after operation. GM and GE were separately dissolved in 0.9% saline solutions  and administered orally for 5 days and EFS stress was administered before EEG recording. Treatment with saline and bean GE was performed 10 min before the EEG recording. After treatment, rats were immediately connected to EEG and EMG recording cables (two EEG channels and one EMG channel). The software discriminates wakefulness as high-frequency low-amplitude EEG, and NREM was scored on the basis of the presence of spindles interspersed with slow waves in the EEG. EEG power during REM was significantly reduced in lower frequency δ-wave (0.75–4 Hz) and increased in the range of θ-wave activity .The brain tissue samples were fixed in 4% formaldehyde  and 30 µm coronal sections were in 0.3% hydrogen peroxide  for 10 min to eliminate endogenous peroxidase activity and washed with phosphate-buffered saline containing 0.2% tween-20 (PBST). The brain tissue samples were incubated with 3% BSA  and 0.2% triton x-100 at room temperature (RT) for 1 h to block nonspecific binding; then, brain tissue samples were rinsed with PBST and incubated at 4 °C overnight with primary antibodies against CRF (1:500), c-Fos (1:1000), and 5-HT (1:500). The following day, the tissues were washed with PBST and incubated with a secondary antibody  at RT for 2 h. The signal was visualized using an ABC kit  and DAB substrate kit . After washing with PBS for 10min, the sample tissues were imaged using a light microscope.p-value ≤ 0.05.All results were analyzed using IBM SPSS 23.0  and presented as mean ± standard error of the mean (SEM). Statistical comparisons were performed for the EEG and immunological studies using one-way analysis of variance (ANOVA), followed by Tukey tests. Differences were considered significant at a


Despite the risks of venous thromboembolism (VTE) in surgical patients are well defined, primary thromboprophylaxis (TP) can be neglected. The aim of this study was to evaluate the risk of VTE and appropriateness of TP and to assess the effects of education and clinical pharmacy (CP) services.n = 800): pre-education (n = 340), post-education (n = 269) and CP intervention period (n = 191) and the risk of VTE and the appropriateness of TP were evaluated. At the end of pre-education period, patients were re-evaluated after education was given about the guidelines on TP and an educative poster was posted in the services (post-education period). During the CP intervention period, the CP made recommendations in terms of optimal TP use to the physicians in charge.This study was conducted in a total of 3 periods (p = 0.238); this rate was increased to 113/191 (59.2%) in the CP intervention period (p = 0.004). High-risk patients who received one type of TP constituted the majority of patients who did not receive optimal TP. While the ratio of high-risk patients undergoing a single type of TP in the pre- and post-education periods , was similar (p = 0.819); with the CP interventions, this rate was reduced to 35/191 (18.3%) (p = 0.001).While there was no significant difference in the optimal TP rate administered to the patients before and after education (138/340, 40.6% vs. 122/269, 45.4%; Even though education has positive influence on surgeons, the implementation of CP practices is more effective especially in terms of maintaining optimal TP. Thromboprophylaxis (TP) is recommended in risky hospitalized patients. In the absence of TP, the incidence of asymptomatic deep vein thrombosis (DVT) is 15–80% in surgical and trauma patients and 10–40% in medical patients. Diagnosis and treatment of both the disease itself and its complications  are difficult and high-cost   in the pre-education period was significantly longer than the post-education period [median (range): 5 (1–275) and 4 (1–288) days; 2 (67.1%), history of major surgery (64.0%), 41–60 years of age (45.6%), history of cancer (45.4%), and length of surgery over 2 h (25.4%) were detected as most common risk factors for VTE. According to the total score, 5.3% of the patients were at low-risk, 24.5% moderate-risk, and 70.2% high-risk. Also, there was no statistically significant difference between the groups (p > 0.05) , the clinical pharmacist made a recommendation for optimal TP administration to a total of 118 patients, including 2 (66.7%) low-risk patients, 28 (55.0%) moderate-risk patients, and 88 (64.2%) high-risk patients. The acceptance rate of clinical pharmacist recommendations by surgeons was 50% in low-risk, 50% in moderate-risk, 58% in high-risk, and 56% in all patients.During the intervention period (p = 0.039). In high-risk patients, there was a significant increase in optimal TP rates in the clinical pharmacist-intervention period compared to the post-education period . When all periods were compared, the optimal TP administration rate, which was 40.6% in the pre-education period, increased to 45.4% (p = 0.238) in the post-education period and increased to 59.2% in the clinical pharmacist-intervention period (p = 0.004). On the other hand, optimal TP from the first and third periods were also compared to assess the contribution of education and clinical pharmacist interventions together, there was a significant increase in optimal TP rates for patients with high risk  . Any TP was administered at a rate of 65.8% in the pre-education period to patients with moderate-risk, and this rate increased to 78.4% in the clinical pharmacist-intervention period p = 0.09. In higp = 0.001). In all classes with low, medium and high Caprini risk, the appropriateness of the selected TP method increased in the clinical pharmacist-intervention period compared to the post-education period; this increase was statistically significant in high-risk patients . While the proportion of patients for whom the selected TP method was partially appropriate was similar in the pre- and post-education periods, it decreased statistically significantly in the clinical pharmacist-intervention period compared to the post-education period  The effect on the appropriateness of TP of patients under any TP was found to be significantly increased following education and clinical pharmacist interventions in high-risk patients  . In total, it was found that the rate of optimal TP administration was lowest in patients undergoing minor surgery (23.2%), but this rate was higher in patients undergoing laparoscopic surgery (59.6%) in all periods.When optimal TP administration was evaluated according to the minor, major, and laparoscopic surgery, no significant difference was observed between the groups (p = 0.001). The rate of high-risk patients receiving mechanical prophylaxis alone, which was 27.1% in the post-education period, decreased statistically significantly to 12% in the clinical pharmacist-intervention period (p < 0.001). The rate of patients receiving inappropriate doses of pharmacological prophylaxis decreased significantly in the post-education period compared to the pre-education period . As a result, with the combined contribution of education and clinical pharmacist interventions, a significant decrease was found in the rate of high-risk patients with a single type of TP (p = 0.001), inappropriate duration (p = 0.004) and dosage of pharmacological TP (p = 0.018)  TP lasting more than 28 days.During the 30-day follow-up period, VTE was observed in 4 (1.2%) patients in the pre-education and 1 (0.4%) patient in the post-education period. No VTE was observed in any patient in the clinical pharmacist-intervention period. In the pre-education period, it was determined that the risk of VTE was high in 3 patients , moderate in 1 patient  and optimal TP was administered to all of these patients. Major post-operative bleeding was observed in 6 (1.8%) patients in the pre-education period, 5 (1.9%) patients in the post-education period, and 2 (1.0%) patients in the clinical pharmacist-intervention period. It was determined that pharmacological prophylaxis was not administered in 1 of 13 patients with major bleeding and this patient was in the post-education period. In 1 of the 2 patients with major bleeding in the clinical pharmacist-intervention period, it was observed that the clinical pharmacist's recommendation was not implemented and high-dose pharmacological prophylaxis treatment was continued.In this study, VTE risk and prophylaxis in hospitalized and operated patients in a tertiary referral hospital were evaluated in pre-education, post-education, and clinical pharmacist-intervention periods. Although there are studies in the literature –10 evalu2 (19.6%) [2 (67.1%) and cancer (43.4%) were higher. It is estimated that due to the COVID-19 pandemic, minor surgeries were postponed and complicated surgeries were performed at a relatively higher rate as our hospital is one of the largest tertiary referral hospitals.According to the DissolVE-2 study in which TP was evaluated, the most common statements of Caprini RAM were major surgery (52.6%), 41–60 years of age (45.4%), cancer (27.5%), laparoscopic surgery (23.7%), minor surgery (21.5%), and BMI > 25 kg/m (19.6%) . In our In our study, the higher rate of patients with high-risk of VTE 70.2%) compared to the literature –13 may b% compareThe rate of moderate-risk patients who administered any TP increased to 78.4% in the clinical pharmacist-intervention period, resulting in a significant improvement compared to the education-based literature . There ip < 0.001) [Studies have shown that the rate of patients who did not receive any TP in surgical patients was 28.1–77.5% –19. In a< 0.001) . Althougth ACCP guideline, we found that inappropriate dosage of pharmacological prophylaxis (2.1%) decreased significantly with education (p = 0.005). In two studies, in which compliance with the 6th and 7th ACCP guidelines between 2008 and 2009 was examined in TP administration, the rate of patients receiving inappropriate doses of pharmacological prophylaxis was 9.3% and 10.9%, respectively [According to the 9ectively , 19. TheAlthough many guidelines recommend prolonged TP in high-risk patients undergoing abdominal pelvic cancer surgery, in our study, the rate of these patients who received the ACCP-recommended TP was found to be low (29%).The use of intermittent pneumatic compression (IPC) devices for VTE prophylaxis is appropriate when used according to the latest clinical recommendations . In our The important limitations of our study are that the education was not repetitive, the risk assessment was performed only in the pre-operative period, and the inadequacy of the IPC devices.In our study in which VTE risk and prophylaxis were evaluated, inappropriateness in optimal TP administration were pointed out. In operated patients, it is important to consider the Caprini RAM and guidelines in the process of assessing the risk of VTE and making the optimal TP decision. In our study, it was demonstrated that interventions and education resulted in an improvement in optimal TP. The results of our study need to be supported by larger prospective randomized controlled trials with the implementation of clinical pharmacy services.